Smart Home Devices Research Articles

CPUs and GPUs in Smart Home Devices: Performance, Applications, and Future Directions

This paper explores the performance and applications of central processing units (CPUs) and graphics processing units (GPUs) in smart home devices. It delves into the background and trends of smart home evolution, highlighting the applied technologies and the roles of CPU and GPU. The literature review presents advancements in CPU and GPU technologies for smart home devices, including their developments in home data processing, image recognition, and device interconnection. The methodology section discusses the architecture design of CPU and GPU in smart home devices, emphasizing multi-core design, parallel processing, and cache optimization. The applications of CPUs and GPUs are examined in smart home appliances and home entertainment systems, including their roles in device control, energy efficiency management, intelligent operation interfaces, 4K video processing, game graphics rendering, and intelligent recommendation systems. The experiment evaluates the performance of CPUs and GPUs in smart home devices through a simulated home environment and specific performance indicators. Finally, the conclusion emphasizes the importance of high-performance computing chips in smart home devices. It suggests future research directions, including optimizing performance, integrating AI technology, and developing new computing chips.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Understanding Wireless Debugging with Sniffers in Smart Home Environments

Wireless communication is becoming increasingly prevalent in smart home environments; the need for effective debugging methodologies has become paramount for system reliability and performance optimization. This comprehensive article analysis explores the implementation of wireless sniffers across multiple protocols, including Wi-Fi, Bluetooth, and Zigbee, with particular emphasis on their application in contemporary IoT devices. Through a detailed examination of chipset capabilities from major manufacturers, including Qualcomm Atheros, Texas Instruments, and Nordic Semiconductor, this article presents a structured approach to wireless debugging that encompasses packet capture mechanisms, signal analysis, and protocol verification. The investigation demonstrates how modern sniffer technologies can effectively diagnose common issues such as packet loss, signal interference, and protocol misconfigurations in smart home devices, including voice assistants, security systems, and environmental controls. By synthesizing practical implementation strategies with technical analysis, this article provides developers and system architects with actionable methodologies for improving wireless system reliability while highlighting the critical role of protocol analysis in ensuring robust IoT deployments.

Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM

In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model’s real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments.

Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning

This paper introduces JEMADAR-AI, an approach to energy management within smart grids, leveraging an edge-cloud continuum architecture coupled with Deep Q-Learning to optimize the operation of smart home devices. The main hypothesis of this work is that combining advanced machine learning models with edge-cloud computing can significantly improve energy efficiency and cost savings in smart grids. The proposed system utilizes SARIMA for seasonal trends and LSTM for long-term dependency models to forecast energy consumption and production, enabling proactive decision-making to balance supply and demand in real-time. JEMADAR-AI employs a deep reinforcement learning algorithm (Deep Q-Learning) to optimize appliance operations, dynamically adjusting energy usage based on predicted demand and supply fluctuations. This ensures that household energy consumption aligns with production capabilities, particularly during periods of renewable energy generation. The architecture combines the high processing power of cloud computing for long-term forecasting with the low-latency responsiveness of edge computing for real-time appliance control. This Edge-Cloud Continuum approach provides an efficient solution for managing energy in distributed smart grids. The experimental results, obtained using Gridlab-D and Omnet++ simulations, demonstrate that JEMADAR-AI improves decision-making speed by 32.25% and reduces household energy bills by 22.11% compared to traditional cloud-based systems.

Global Visualization and Contemporary Movements of Smart Home Devices: A Bibliometric Analysis

Global Visualization and Contemporary Movements of Smart Home Devices: A Bibliometric Analysis

Secure Search of Smart Home Data Based on Access Control Encryption

Abstract: In recent years, the increasing variety of smart home devices, such as smart locks, surveillance cameras, and various sensors, has led to a significant rise in related data. To facilitate the sharing of smart home data, manufacturers store these data on the Internet of Things (IoT). However, since IoT servers are not fully trustworthy and the data often contain sensitive commercial information, any leakage could result in a commercial crisis for manufacturers. To address security concerns, manufacturers encrypt the data before storing them on the IoT. A challenge arises: how can users search for target data in encrypted form? This paper proposes a new search encryption scheme for smart home data, addressing the limitations of existing attribute-based encryption (ABE) searchable encryption schemes. The proposed scheme integrates access control trees and bilinear mapping technology, supporting multiple access policies including AND gates, OR gates, and threshold gates. Experimental results demonstrate a comprehensive analysis of the attribute-based searchable encryption protocol under a multi-authority architecture, covering four dimensions: correctness, security, complexity, and real-world application scenarios. In specific scenarios, when a data user, out of privacy concerns, is unwilling to reveal all attribute information to a single authority during a query for shared datathus avoiding identity disclosureor when the users identity information is managed by multiple independent authorities, this protocol proves highly effective. It ensures smooth data sharing while maintaining data security for both data owners and users, fully safeguarding data privacy and shareability.

Management of diverse smart homes: Coordination among controllable loads, EVs, and PVs

Growing energy demand, driven by urbanization, transportation electrification, and smart home devices, highlights the significance of residential electricity consumption. However, controllable loads, including HVAC systems, water heaters, electric vehicles (EVs), and industrial processes, can be managed to balance electricity supply and demand. Therefore, this study addresses the escalating energy demand in residential buildings including the integration of electric vehicles (EVs) and renewable energy technologies. It focuses on controllable loads, which offer flexibility that can be managed to balance supply and demand through demand response programs. First, the controllable loads are categorized as HVAC (air conditioners, electric heaters, and electric heat pumps), uninterruptable (washing machines, dishwashers, and electric cookers), interruptible (water heater), and EVs. Then, this study proposes a detailed clustering approach for homes, categorizing them into six clusters to capture diverse consumption patterns, including scenarios such as work-from-home setups. The analysis includes examining the adaptability of these home clusters and controllable load groups under different EV charging levels (level 1 and level 2) and coordinating them with PV systems. The study evaluates the coordination of each controllable load group with EVs and PVs, aiming to optimize overall energy usage and enhance renewable resource integration. Parameters such as energy management, operation of controllable equipment, and indoor temperature are analyzed to provide insights for homeowners and policymakers to make informed decisions about load management and energy resource utilization in residential settings. Simulation results show that with level 1 chargers, type 2 homes achieve higher average EV avoidance (81.94 %) and lower PV consumption (31.22 %), while with level 2 chargers, all homes reach 100 % EV avoidance.

“They Didn’t Buy Their Smart TV to Watch Me with the Kids”: Comparing Nannies’ and Parents’ Privacy Threat Models for Smart Home Devices

Smart home devices raise privacy concerns among not only primary users, but also bystanders like domestic workers. We conducted 25 qualitative interviews with nannies and 16 with parents who employed nannies, in the US, to explore and compare their views on and privacy threat models for smart home devices. We found device-specific purposes of use inspired different perspectives among nanny participants. Most were comfortable with employers’ smart speakers and smart TVs, whose purpose had nothing to do with them. However, with indoor smart cameras, nanny participants were often not just bystanders but targets of monitoring; in such situations, they had a wider range of attitudes. In contrast, parent participants tended to have more similar views across devices. We found notable disconnects regarding disclosure, where nanny participants often hesitated to ask about cameras, but parent participants assumed nannies just didn’t care. We recommend prioritizing interventions supporting disclosure, discussion, and sharing control.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Values as causes of emotions and acceptability in the digital risk context: an extension of the values scale with privacy

Values have been proposed to be important predictors of emotions about products, which in turn have been proposed to be associated with acceptability of these products. While support for this theory has been found in the environmental domain, it has not been studied in other domains, such as the domain of digital risks. The main objective of this study was to examine if three values (hedonism, personal security, and privacy) are predictors of emotions about a smart home device (a smart speaker), and if, in turn, those emotions influence the acceptability of the device (operationalized as adoption attitude). Additional objectives were to test whether the relation between risk-related values and emotions strengthens when people receive a value-tailored warning, and whether privacy as a value is a valuable addition to the 19 value scale by Schwartz and colleagues. With a scenario study (N = 623), we found, first, that privacy as a value could indeed be distinguished from the other values. Second, the values hedonism and privacy, but not personal security, predicted either or both positive and negative emotions towards a smart device, and in turn the acceptability of the smart device. Third, the effects of the values privacy and personal security on emotions were not strengthened by a value-tailored warning. Fourth, two additional values were identified that also explained emotions and in turn acceptability of the smart device, being power-resources and tradition. Fifth, privacy was found to be a valuable addition to the 19 value measurement, which explained emotions over and above the other 19 values. The study thus shows that values are also important predictors of emotions and in turn acceptability in the context of digital risks, with privacy as an important newly developed value that contributes to a more in-depth understanding of consumer decision making in this context.

Prediction of the Future Trends of Housing Project Development with Modified Delphi Technique

This research was initiated to study the trend of the housing projects in the next ten years (2022-2031), with the applied Modified Delphi Technique. The research was conducted by interviewing five experts with more than ten years of knowledge and experience in design, construction, technology, location, and marketing. The gathered trends were applied to create questionnaires in order to conduct two rounds of surveys to assess the consensus level of ten experts with at least ten years of experience in the real estate industry, according to the consensus analysis method from the Modified Delphi Technique. Thirteen trends were found from the study. There are four design trends: more beautiful façade, soundproof wall, universal design furniture, and universal design house building. In addition, eco-friendly materials and modular construction systems were found to be construction trends while smart home device installation and active air flow technology were found to be technology trends. Regarding the trend of the project location, the projects tend to be located in areas expanding into the suburbs. Finally, four marketing trends were also reported: Virtual Reality utilization in sales presentation, advertising to reach more customers, inviting social media influencers to play a role in reviewing projects, providing credit consulting services and refunding of reservation money if a customer’s loan is not approved. The results of the study provide information to all parties involved in planning strategies and operating a business in developing housing estates in the future.

"My Best Friend's Husband Sees and Knows Everything": A Cross-Contextual and Cross-Country Approach to Understanding Smart Home Privacy

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

Optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs

Abstract In this paper, a two-layer model is proposed for smart devices in a home energy management system in order to optimize residential energy use under time-of-use tariffs and home microgrids. Home smart devices are modelled based on their power consumption attributes and building thermal resistance. An HVAC-transferable device cooperative operation strategy is introduced to construct the economic optimization of the upper-level HVAC operation and the lower-level transferable device operation. This strategy reduces home power costs while maintaining user comfort. By iteratively solving the two-layer model, the optimal power consumption scheme for home smart devices is determined.

Anomaly detection and prediction of energy consumption for smart homes using machine learning

AbstractAs technology advances, smart homes are being increasingly adopted, thus generating massive data that pose new research challenges. We propose a machine learning framework for monitoring energy consumption in smart home devices. The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home. We employ three outlier‐based techniques for anomaly detection: (1) local outlier factor, (2) connectivity‐based outlier factor, and (3) cluster‐based local outlier factor. Furthermore, we apply random forest, linear regression, decision tree, and the ensemble techniques of adaptive, gradient, and extreme gradient boosting to anomaly free data to develop baseline models that predict the energy consumption patterns of smart home devices. The framework is evaluated on three publicly available energy datasets collected from various smart homes. The experimental results reveal that the cluster‐based local outlier factor with extreme gradient boosting achieves promising results with high prediction accuracy.

An adaptive approach for integrating primary user behavior in compressive spectrum sensing

Compressive sensing (CS) has been widely used to sense the wideband spectrum with fewer measurements by taking advantage of radio spectrum underutilization. As new smart devices, such as IoT devices, smart home devices, and wearables, use batteries and have limited memory, more research is needed to reduce the overuse of cognitive radio (CR) resources through spectrum sensing. To reduce the number of compressive measurements required for spectrum recovery, researchers proposed approaches like weighted and sequential compressive sensing. In this paper, we estimate the primary user’s (PU) behavior statistics and use the estimated information in a novel weighted sequential compressive spectrum sensing approach. Our proposed approach can reduce and adapt the number of measurements and the sensing time to the changing number of active channels in a dynamically changing wideband spectrum.

Optimizing radiology remote reading: leveraging technology to improve efficiency.

Remote work has been increasingly utilized in the profession of radiology over recent years. Setting up your individual workstation offers an opportunity to tailor it to suit your preferences without the restriction of a universal setup to accommodate multiple users. Important considerations when setting up a home workstation include selecting the optimal work location, choosing the proper desk and chair, and configuring an ideal computer monitor layout. The use of peripheral devices, such as programmable mice and hands-free dictation tools can improve efficiency and reduce repetitive strain injuries. This article also explores the use of smart home devices and programmable scripts using AutoHotKey to further streamline workflow and maximize the benefits of a remote workstation.

User-Centric Design in Mobile Application Development for Smart Home Devices

User-Centric Design in Mobile Application Development for Smart Home Devices

Review of Smart-Home Security Using the Internet of Things

As the Internet of Things (IoT) continues to revolutionize the way we interact with our living spaces, the concept of smart homes has become increasingly prevalent. However, along with the convenience and connectivity offered by IoT-enabled devices in smart homes comes a range of security challenges. This paper explores the landscape of smart-home security. In contrast to similar surveys, this study also examines the particularities of popular categories of smart devices, like home assistants, TVs, AR/VR, locks, sensors, etc. It examines various security threats and vulnerabilities inherent in smart-home ecosystems, including unauthorized access, data breaches, and device tampering. Additionally, the paper discusses existing security mechanisms and protocols designed to mitigate these risks, such as encryption, authentication, and intrusion-detection systems. Furthermore, it highlights the importance of user awareness and education in maintaining the security of smart-home environments. Finally, the paper proposes future research directions and recommendations for enhancing smart-home security with IoT, including the development of robust security best practices and standards, improved device authentication methods, and more effective intrusion-detection techniques. By addressing these challenges, the potential of IoT-enabled smart homes to enhance convenience and efficiency while ensuring privacy, security, and cyber-resilience can be realized.

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model

ABSTRACTThe Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain‐based deep learning model for IDS. Initially, the input data are preprocessed using min‐max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM‐FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM‐FCVAE model is analyzed by evaluating several metrics. The proposed GM‐FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW‐NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM‐FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.